Osteoporosis is characterized by a systemic reduction in bone mass leading to increased bone fragility and an increased risk of bone fracture.
Current methods for the early assessment of fracture risk as well as treatment response include non-invasive imaging techniques as well as the analysis of clinical parameters and biochemical markers of bone turnover. Recently, microRNAs have been identified to be secreted into the bloodstream from cells of various tissues, possibly indicating pathological processes in different parts of the body. There is evidence that microRNAs play an important role in the development and function of bone forming and bone resorbing cells, specifically osteoblasts and osteoclasts. Both cell types control the homeostasis between bone anabolism and catabolism, and therefore microRNAs play a pivotal physiological role in bone metabolism. To this day, however, little is known whether an imbalance in bone metabolism, which causes bone diseases, may be reflected in the levels of circulating microRNAs.
Osteoporotic fractures are caused by an increase in bone fragility, which can occur due to low bone mass and microarchitectural changes in bone tissue. Such fractures are the critical hard outcome of osteoporosis, which affects more than 75 million people in the United States, Europe and Japan (Kanis et al., 2013). With a lifetime risk of 30%-40% to be affected by vertebral or non-vertebral fractures in developed countries, osteoporosis has an incidence rate similar to that of coronary heart disease. Furthermore, with the exception of forearm fractures, osteoporotic fractures are associated with increased mortality. Most fractures cause acute pain and lead to patient hospitalization, immobilization and often slow recovery.
In addition, osteoporotic symptoms are frequently observed in patients with type 2 diabetes, who overall suffer from an elevated risk of fragility fractures. Diabetes mellitus refers to a group of metabolic diseases in which a subject has high blood sugar. Type 2 diabetes results from insulin resistance, a condition in which cells fail to use insulin properly, sometimes also with an absolute insulin deficiency. This form was previously referred to as non insulin-dependent diabetes mellitus (NIDDM) or “adult-onset diabetes”.
In the prophylaxis, diagnosis and management of osteoporosis, the assessment of fracture risk and monitoring of treatment response are two of the most important aspects. Therefore, analysis of bone mass by measuring bone mineral density (BMD) is currently the only clinical parameter of the skeleton that is routinely analyzed in clinical practice and part of the WHO FRAX questionnaire (Kanis et al., 2013). However, due to the lacking correlation with bone strength and bone metabolism (Cefalu, 2004), age- and site-dependent differences in bone density, the assessment of the T-Score (i.e. a comparison of a patient's BMD to that of a healthy thirty-year-old) in combination with other established clinical scores of fracture risk (Rubin et al., 2013) often does not improve the prediction of fracture risk. Particularly in case of patients suffering from type-2 diabetes there is no evidence for correlation between BMD and fracture risk, which demonstrates the need for alternative markers of fracture risk.
In order to estimate the rate of bone formation, bone resorption and therapeutic treatment response, few biochemical bone turnover markers (BTM) have been identified (Vasikaran et al., 2011), such as serum procollagen type I N propeptide (s-PINP), serum C-terminal telopeptide of type I collagen (s-CTX). While the correlation of these markers with bone metabolism has been established, their specificity and sensitivity for fracture risk prediction needs to be further validated. Therefore, only few countries have recommended to incorporate these biochemical markers into clinical practice (Vasikaran et al., 2011).
Other potential markers of bone metabolism may be derived from the signaling pathways that are known to play a major role in bone formation and resorption, such as WNT, BMP-2 or RANKL. For example, proteins derived from Dickkopf-1 (DKK-1) or Sclerostin (SOST) genes can act as binding partners of WNT and WNT-receptors, thereby regulating its activity and subsequently bone formation (Canalis, 2013). However, the pre-analytical stability of these proteins in serum/plasma in response to diet, exercise and circadian rhythm is questionable, and so is the general significance for bone metabolism due to the fact that these proteins are produced in other tissues as well and might be regulated in response to other diseases. Especially in respect to certain types of cancer, WNT-signalling has been shown to drive the progression of disease (Anastas & Moon, 2013).
Recently, increased attention has been attributed to the importance of microRNAs (miRNAs), small non-coding RNAs that regulate gene expression (Bartel, 2009), in the control of bone metabolism (Dong, Yang, Guo, & Kang, 2012; Zhao et al., 2013). Several miRNAs have been shown to silence osteogenic inhibitors during stem cell differentiation into osteocytes (Trompeter et al., 2013), to regulate BMP2-mediated osteoblast proliferation and differentiation (Li et al., 2008), or to orchestrate the activity of WNT-signalling (Kapinas, Kessler, & Delany, 2009). Therefore, the potential of miRNAs as therapeutic agents for accelerating bone regeneration and/or as diagnostic tools for evaluating bone metabolism and fracture risk has recently been acknowledged (van Wijnen et al., 2013). The impressive stability of miRNA in serum and plasma even after being subjected to harsh conditions, the limited number of miRNAs (<500 found secreted in plasma/serum), their simple chemical composition, the lack of posttranscriptional modification and the availability of advanced and well established, highly sensitive screening techniques define miRNAs as excellent candidates for biomarkers. In fact, blood-circulating miRNAs have already been analyzed in the context of disease (Keller et al., 2011), especially cancer and cardiovascular disease, or non-pathological processes such as ageing (Weiner et al., 2013). A combination of miRNAs that can control the onset and progression of osteoporosis or can serve as surrogate markers for this pathological process, is a specific osteoporosis signature whose use would represent a non-invasive approach to predict the fracture risk as well as targets for therapeutic control of the progression of osteoporosis.
WO2013155085 suggests a diagnostic method for low bone mineral density that detects hsa-miR-133a in monocytes.
Recently, five freely circulating miRNAs and bone tissue miRNAs have been identified and implicated with osteoporotic fractures (Seeliger et al., 2014).
WO2007023306 describes the use of miRNA-223 for diagnosis of a bone disease.
Wang Y. et al., 2012, PlosOne, 7,4, e34641 report miR-133a as potential biomarker associated with postmenopausal osteoporosis.
WO2011144761 describes miR-31 for use in the treatment of bone disorders.